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Carbon Taxes, Path Dependency, and Directed
Technical Change: Evidence from the Auto Industry
Philippe Aghion, Antoine Dechezleprêtre, David Hémous, Ralf Martin, John
van Reenen
To cite this version:
Philippe Aghion, Antoine Dechezleprêtre, David Hémous, Ralf Martin, John van Reenen. Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry. Journal of Political Economy, University of Chicago Press, 2016, 124 (1). �halshs-01496920�
Carbon Taxes, Path Dependency, and Directed
Technical Change: Evidence from the
Auto Industry
Philippe Aghion
Harvard University, National Bureau of Economic Research, and Canadian Institute for Advanced Research
Antoine Dechezlepreˆtre
Grantham Research Institute and Centre for Economic Performance, London School of Economics
David He´mous
INSEAD
Ralf Martin
Imperial College, Grantham Institute, and Centre for Economic Performance, London School of Economics
John Van Reenen
Centre for Economic Performance, London School of Economics, and National Bureau of Economic Research
Can directed technical change be used to combat climate change? We construct new firm-level panel data on auto industry innovation
distin-guishing between “dirty” ðinternal combustion engineÞ and “clean”
ðe.g., electric, hybrid, and hydrogenÞ patents across 80 countries over We would like to thank the editor, two anonymous referees, Daron Acemoglu, Robin Burgess, Michael Greenstone, John Hassler, Rebecca Henderson, Ken Judd, Per Krusell,
Electronically published January 5, 2016 [ Journal of Political Economy, 2016, vol. 124, no. 1]
several decades. We show that firms tend to innovate more in clean ðand less in dirtyÞ technologies when they face higher tax-inclusive fuel prices. Furthermore, there is path dependence in the type of innova-tion ðclean/dirtyÞ both from aggregate spillovers and from the firm’s own innovation history. We simulate the increases in carbon taxes needed to allow clean technologies to overtake dirty technologies.
I. Introduction
There is a wide scientific consensus that greenhouse gas emissions from
human activities, in particular carbon dioxide ðCO2Þ, are responsible for
the current observed warming of the planet. Automobiles are major con-tributors to these emissions: according to the International Energy Agency,
in 2009 road transport accounted for 4.88 gigatons of CO2, which
repre-sented 16.5 percent of global CO2emissions ðtransport as a whole was
re-sponsible for 22.1 percentÞ. In this paper we look at technological innova-tions in the auto industry and examine whether government intervention can affect the direction of this innovation. More specifically, we construct a new panel data set on auto innovations to examine whether firms redi-rect technical change away from dirty ðpollutingÞ technologies and to-ward cleaner technologies in response to increases in fuel prices ðour proxy for a carbon taxÞ in the context of path-dependent innovation. We
associate“dirty” innovation with internal combustion engine patents and
“clean” innovation with electric, hybrid, and hydrogen vehicle patents, but we discuss carefully issues around this definition and consider various
alternatives.1
Our main data are drawn from the European Patent Office’s ðEPOÞ World Patent Statistical database ðPATSTATÞ. These data cover close to the population of all worldwide patents since the mid-1960s. Our
out-come measure focuses on high-value“triadic” patents, which are those
that have been taken out in all three of the world’s major patents offices:
the EPO, the Japan Patent Office ð JPOÞ, and the US Patents and
Trade-1 We do not consider radical innovations in upstream industries such as biofuels, for
in-stance. To explore this is beyond the scope of the current paper, which takes the more pos-itive approach of analyzing the determinants of clean innovation in vehicles.
Ed Lazear, Torsten Persson, David Popp, Esteban Rossi-Hansberg, Nick Stern, and Scott Stern for helpful comments. Participants at seminars in the American Economic Associa-tion, Arizona, Birmingham, Cambridge, Canadian Institute for Advanced Research, Impe-rial, INSEAD, London School of Economics, Manchester, National Bureau of Economic Research, Paris, Stanford, Stirling, Stockholm, and Venice have all contributed to improv-ing the paper. Financial support has come from the British Academy, the Economic and Social Research Council through the Centre for Economic Performance and the Centre for Climate Change Economics and Policy, and the Grantham Foundation for the Protec-tion of the Environment. Antoine Dechezleprêtre gratefully acknowledges the support of the ESRC under the ESRC Postdoctoral Fellowship Scheme ðaward PTA-026-27-2756Þ. Xavier Vollenweider and Chris Gaskell provided excellent research assistance. Data are provided as supplementary material online.
mark Office ðUSPTOÞ. Our database also reports the name of patent ap-plicants, which in turn allows us to match clean and dirty patents with dis-tinct patent holders each of whom has her own history of clean versus dirty patenting. Finally, we know the geographical location of the inven-tors listed on the patent so we can examine location-based knowledge spillovers.
We report three important empirical findings. First, higher fuel prices induce firms to redirect technical change away from dirty innovation
and toward clean innovation. Second, a firm’s propensity to innovate
in clean technologies appears to be stimulated by its own past history of clean innovations ðand vice versa for dirty technologiesÞ. In other words, there is path dependence in the direction of technical change: firms that have innovated a lot in dirty technologies in the past will find
it more profitable to innovate in dirty technologies in the future.2Our
third finding is that a firm’s direction of innovation is affected by local
knowledge spillovers. We measure this using the geographical location of its inventors. More specifically, a firm is more likely to innovate in clean technologies if its inventors are located in countries where other firms have been undertaking more clean innovations ðand vice versa for dirty technologiesÞ. This provides an additional channel that reinforces path dependency.
Our paper relates to several strands in the literature. First, our work is
linked to the literature on climate change, initiated by Nordhaus ð1994Þ.3
We contribute to this literature by focusing on the role of innovation in mitigating global warming and by looking at how various policies can in-duce more clean innovation in the auto industry.
We also connect with work on directed technical change, in particular, Acemoglu ð1998, 2002, 2007Þ, which itself was inspired by early
contribu-tions by Hicks ð1932Þ and Habakkuk ð1962Þ.4We contribute to this
liter-4 The theoretical literature on directed technical change is well developed. For
applica-tions to climate change, see, e.g., Messner ð1997Þ, Grübler and Messner ð1998Þ, Goulder and Schneider ð1999Þ, Nordhaus ð2002Þ, van der Zwaan et al. ð2002Þ, Buonanno, Carraro, and Galeotti ð2003Þ, Smulders and de Nooij ð2003Þ, Sue Wing ð2003Þ, Manne and Richels
2 As shown in Acemoglu et al. ð2012Þ, this path dependency feature when combined
with the environmental externality ðwhereby firms do not factor in the loss in aggregate productivity or consumer utility induced by environmental degradationÞ will induce a laissez-faire economy to produce and innovate too much in dirty technologies compared to the social optimum. This in turn calls for government intervention to“redirect” tech-nical change.
3 Nordhaus ð1994Þ developed a dynamic Ramsey-based model of climate change ðthe
dynamic integrated climate-economy [DICE] modelÞ, which added equations linking pro-duction to emissions. Subsequent contributions have notably examined the implications of risk and discounting for the optimal design of environmental policy. In particular, see Stern ð2006Þ, Nordhaus ð2007Þ, Weitzman ð2007, 2009Þ, Dasgupta ð2008Þ, Mendelsohn et al. ð2008Þ, von Below and Persson ð2008Þ, and Yohe, Tol, and Anthoff ð2009Þ. Recently, Golosov et al. ð2014Þ have extended this literature by solving for the optimal policy in a full dynamic stochas-tic general equilibrium framework.
ature by providing empirical evidence on the role of carbon prices in di-recting technical change. Earlier work by Popp ð2002Þ is closely related
to our paper. This paper uses aggregate US patent data from 1970–94 to
study the effect of energy prices on energy-efficient innovations. Popp finds a significant impact from both energy prices and past knowledge stocks on the direction of innovation. However, since he uses aggregate data, a concern is that his regressions also capture macroeconomic
shocks correlated with both innovation and the energy price.5The
nov-elty of our approach is that we use international firm-level panel data
and exploit differences in a firm’s exposure to different markets to build
firm-specific fuel prices, which allows us to provide microeconomic evi-dence of directed technical change. Acemoglu et al. ð2016, in this issueÞ calibrate a microeconomic model of directed technical change to derive quantitative estimates of the optimal climate change policy. The focus of our work is more empirical, but we use our results to perform a related exercise: we simulate the aggregate evolution of future clean and dirty knowledge stocks and analyze how this evolution would be affected by changes in carbon taxes.
Finally, we draw on the extensive literature in industrial organization that estimates the demand for vehicles ðenergy-efficient and otherwiseÞ
as a function of fuel prices and other factors.6We go beyond this work by
looking at the rate and direction of innovation.
The paper is organized as follows. Section II develops a simple model to guide our empirical analysis and Section III presents the econometric methodology. The data are presented in Section IV with some descrip-ð2004Þ, Gerlagh ð2008Þ, Gerlagh, Kverndokk, and Rosendahl ð2009Þ, and Gans ð2012Þ. In contrast, empirical work on directed technical is scarcer; but see Acemoglu and Linn ð2004Þ for evidence in the pharmaceutical industry, Acemoglu and Finkelstein ð2008Þ in the health care industry, or, more recently, Hanlon ð2015Þ for historical evidence in the textile industry.
5 Further evidence of directed technical change in the context of energy saving can be
found in the study by Newell, Jaffe, and Stavins ð1999Þ, who focus on the air conditioning industry, or by Crabb and Johnson ð2010Þ, who also look at energy-efficient automotive technology. Haščič et al. ð2009Þ investigate the role of regulations and fuel price on auto-motive emission control technologies. Hassler, Krussell, and Olovsson ð2012Þ find evidence for a trend increase in energy-saving technologies following oil price shocks. They measure the energy-saving bias of technology as a residual, which is attractive as it sidesteps the need to classify patents into distinct classes. On the other hand, our technology variables are more directly related to the innovation we want to measure.
6 For example, using around 86 million transactions, Alcott and Wozny ð2014Þ find that
fuel prices reduce the demand for autos, but by less than an equivalent increase in the ve-hicle price. They argue that this is a behavioral bias causing consumers to undervalue fuel price changes. Readers are referred to this paper for an extensive review of the literature on fuel prices and the demand for autos. Busse, Knittel, and Zettelmeyer ð2013Þ use similar data in a more reduced-form approach but, by contrast, find a much larger impact of fuel price on auto demand. Although the magnitude of the fuel price effect on demand differs between studies, it is generally accepted that there is an important effect of fuel prices on vehicle demand.
tive statistics. Section V reports the results and discusses their robustness and some extensions. We perform the simulation exercise in Section VI. Section VII presents conclusions. Appendix A provides details on the theoretical model, appendix B on the econometrics model, and appen-dix C on the data. All appendices are available online.
II. Theoretical Predictions
In this section we develop theoretical predictions that will guide our em-pirical analysis. Full details are in appendix A. We consider a one-period model of an economy in which consumers derive utility from an outside good and from motor vehicle services. To abstract from income effects,
utility is quasi-linear with respect to the outside good C0ðchosen as the
numeraireÞ.
To consume motor vehicle services, consumers need to buy cars and
fuel ðcall this a “dirty car bundle”Þ or cars and electricity ðcall this a
“clean car bundle”Þ. Utility is then given by
U 5 C01 b b 2 1
E
1 0 Yciðj21Þ=jdi ½j=ðj21Þ½ðε21Þ=ε 1E
1 0 Ydiðj21Þ=jdi ½j=ðj21Þ½ðε21Þ=ε½ε=ðε21Þ½ðb21Þ=b ;where the consumption of variety i of clean cars together with the corre-sponding clean energy ðelectricityÞ is
Yci5 minðyci; ycieciÞ;
and the consumption of variety i of dirty cars together with the corre-sponding dirty energy ðfuelÞ is
Ydi5 minðydi; ydiediÞ:
The term eziis the amount of energy consumed for variety i of a type z
car, where z5 c, d, that is, z 5 Clean, Dirty; ε is the elasticity of
substitu-tion between the clean and dirty cars; j is the elasticity of substitution
among varieties within each type of car; and b is the elasticity of
con-sumption of motor vehicle services with respect to its index price ðthis parameter measures the degree of substitutability between motor
vehi-cle services and the outside goodÞ. Finally,yciðrespectively, ydiÞ is the
en-ergy efficiency of clean ðrespectively, dirtyÞ cars. We impose the following
parameters restrictions: 1< ε ≤ j, so that clean cars are more
substitut-able with each other than with dirty cars; andε > b, that is, the elasticity
of substitution between clean and dirty cars is larger than the price
ticity for motor vehicle services ðwhich implies that the elasticity of sub-stitution between clean and dirty cars is larger than that between motor vehicle services and the outside goodÞ.
Varieties of cars are produced by monopolists. Each monopolist owns a given number of varieties in clean or dirty cars of mass zero. The
mo-nopoly producer of variety i of a type z car produces Azicars using one
unit of outside good as an input, and the energy requirement for that
variety is yzi. Therefore, yzi captures energy-augmenting technologies,
while Azi captures technologies that augment the other inputs ðlabor,
for instanceÞ for a car of type z. Prior to production, monopolists can spend R&D resources to increase the level of their technologies ðwe as-sume that the cost function is quadratic in the amount of technological
improvementÞ. We refer to increases in Adi as“dirty” innovations: such
an innovation reduces the price of dirty cars and increases the demand
for fossil fuel, generating more emissions. Increases inydi are“grey”
in-novations; they reduce the amount of emissions per unit of “dirty car
bundles” but they also increase the demand for dirty cars ðthrough a
“re-bound” effectÞ, so that the impact on emissions is ambiguous. Increases
inycior Aciare clean innovations; they lead to a substitution from dirty
cars consumption to clean cars consumption, leading to a decrease in
emissions.7
The model is solved in appendix A. We show that for typical parameter values we can derive some key predictions.
Prediction 1. An increase in the price of the fossil fuel increases
innovation in clean technologies, decreases innovation in dirty nologies, and has an ambiguous impact on innovation in grey tech-nologies.
Prediction2. Firms with an initially higher level of clean
technol-ogies will tend to innovate more in clean technoltechnol-ogies. Similarly, those with higher initial levels of dirty technologies will tend to innovate more in dirty technologies.
Here, we provide only the intuition for these results. First, on the im-pact of an increase in fuel price on clean innovations ðprediction 1Þ, a higher fuel price makes the dirty bundle more expensive; and since clean and dirty cars are substitutes, this encourages the consumption of clean cars. Since the market share of clean cars is now larger, the re-turn to innovation in clean cars is also larger. For dirty cars, a higher fuel price reduces the market share and therefore profits, discouraging both dirty and grey innovation. However, it also increases the returns from grey innovation as saving on fuel reduces the price of a car bundle more when fuel prices are large. The total impact on grey innovations is there-7 As an increase in productivity increases income, there would be an additional rebound
effect if cars were a normal good.
fore ambiguous ðit is more likely to be negative when the price elasticity
of cars is larger and when clean and dirty cars are closer substitutesÞ.8
Second, on path dependence within firms ðprediction 2Þ, a higher level of dirty technologies implies a larger market share increasing the incen-tives to innovate in dirty technologies. Against this, however, more dirty technologies imply that there are lower marginal benefits to making in-vestments that increase productivity and reduce the prices of a dirty car bundle further. The net effect is positive when the elasticity of substitu-tion is sufficiently large ðso that the market size effect is largeÞ. The same applies to grey and clean technologies.
These predictions are also generated by other models in the literature. Acemoglu et al. ð2012Þ and Gans ð2012Þ study models in which innova-tion can augment a clean or a dirty energy technology and show that a carbon tax ðequivalent here to a higher fuel priceÞ increases innovation in clean augmenting technologies ðto the detriment of dirty energy-augmenting technologiesÞ provided that the two inputs are substitutes. This is similar to the trade-off between clean and dirty innovations in our model. Smulders and de Nooij ð2003Þ and Hassler et al. ð2012Þ consider models in which innovation can augment either ðfossil fuelÞ energy or other inputs that are complementary to it. An increase in the price of energy redirects innovation toward energy-augmenting technology, but since the total amount of innovation may decrease, the net impact on energy-augmenting innovation is ambiguous ðthis is similar to what hap-pens to grey innovations here in our modelÞ.
Our model departs from these models, however, in three main re-spects. First, we simultaneously consider clean, dirty, and grey technolo-gies when looking at path dependence. Second, we allow for firm hetero-geneity. Both aspects are directly relevant to our empirical analysis since it is based on firm-level data, and we identify the role of path depen-dence from the difference in innovation efforts by firms with differing technology levels. Third, we allow for an externality whereby local aggre-gate knowledge in a given technology exogenously contributes to a
firm’s own knowledge stock. This directly delivers the third prediction,
which we take to the data.
Prediction3. Firms innovate more in clean technologies when the
aggregate level of clean technologies is higher in neighboring varieties ðand similarly for dirty technologiesÞ.
8 In app. A, we further show that the impact of an increase in fuel price on innovation is
not the same for all varieties if their productivity levels differ. Indeed, the fuel price in-crease affects relatively less the varieties that have a high level of grey over dirty technolo-gies; therefore, these varieties can increase their market share at the expense of other dirty cars. This has the effect of increasing both dirty and grey innovations. By contrast, both types of innovations are further reduced for varieties with a low grey over dirty technology levels ratio.
III. Econometrics General Approach
Consider the following Poisson specification for the determination of
firm innovation in clean technologies:9
PATC;it 5 expðbC;Pln FPit211 AC;it21Þ 1 uC;it; ð1Þ
where PATC,itis the number of patents applied for in clean technologies
by firm i in year t; AC,itis the firm’s knowledge stock relevant for clean
innovation, which depends on both its own stocks of past clean and dirty innovation and the aggregate spillovers from other firms ðdiscussed
be-lowÞ; uC,itis an error term; expðÞ is the exponential operator; and FPitis
fuel price. We lag prices and knowledge stocks to reflect delayed
re-sponse and to mitigate contemporaneous feedback effects.10 In the
ro-bustness section we show that this functional form is reasonable com-paring it to alternative dynamic representations using other lag structures and the Popp ð2002Þ approach.
The fuel price has independent variation across time and countries primarily because of country-specific taxes, and we show the robustness of our results to using just fuel taxes instead of ðtax-inclusiveÞ fuel prices. The profile of car sales across countries differs between auto firms. For
ex-ample, General Motors has some “home bias” toward the US market,
whereas Toyota has a home bias toward the Japanese market ði.e., they sell more in these countries than one would expect from country and firm observables aloneÞ. Thus, different firms are likely to be differently exposed to tax changes in different countries, and the fuel price has a firm-specific component. This firm-specific difference in market shares across countries could arise because of product differentiation and het-erogeneous tastes or perhaps because of government policies to promote
domestic firms. To take this heterogeneity into account, we use the firm’s
history of patent filing to assess the relative importance of the various mar-kets the firm is operating in and construct firm-specific weights on fuel prices for the corresponding market. Simply put, an unexpected increase in US fuel taxes will have a more salient impact on car makers with a bigger market share in the United States than those with a smaller market share. We discuss this in more detail in Section IV.
9 In our regressions we use an equivalent equation for dirty technologies. We initially
discuss only one of these equations to simplify the notation.
10 In principle, the price should be the firm’s expectation of the future evolution of the
fuel price based on the information set at the time of making the innovation investment decision. Fuel prices appear to be well approximated by a random walk process ðe.g., An-derson et al. 2011; AnAn-derson, Kellogg, and Sallee 2013Þ, so given our assumption that de-cisions are made on t2 1 information, lagged prices should be a sufficient statistic for this expectation. Note that the Anderson result is only for US data, but it seems more generally true in other countries ðe.g., Hamilton 2009; Liu et al. 2012Þ.
We parameterize the firm’s total knowledge stock as AC;it5 bC;1ln SPILLC;it1 bC;2ln SPILLD;it1 bC;3ln KC;it
1 bC;4ln KD;it:
ð2Þ
The firm’s knowledge will likely depend on its own history of
innova-tion, and we denote this as KC,itðfirm’s own stock of clean innovationÞ and
KD,itðfirm’s own stock of dirty innovationÞ.11In addition to building on
their own past innovations, firms will also“stand on the shoulders of
gi-ants,” so we allow their knowledge stock to depend on spillovers from
other firms in both clean ðSPILLC,itÞ and dirty technologies ðSPILLD,itÞ.
We use stocks of economywide patents to construct these country-specific spillover measures. Drawing on the evidence that knowledge has a geo-graphically local component ðe.g., Jaffe, Trajtenberg, and Henderson
1993Þ, we use the firm’s distribution of inventors across countries to
weight the country spillover stocks. In other words, if the firm has many inventors in the United States regardless of whether the headquarters of the firm is in Tokyo or Detroit, then the knowledge stock in the United States is given a higher weight ðsee Sec. IVÞ.
There are of course other factors that may influence innovation in ad-dition to fuel prices and the past history of innovation. These include government R&D subsidies for clean innovation, regulations over emis-sions, and the size and income level of the countries a firm is expecting to sell to ðproxied by GDP and GDP per capitaÞ. We denote these
poten-tially observable variables by the vector wC,it. We also allow for
unobserv-able factors by introducing a firm fixed effect ðhC,iÞ, a full set of time
dum-mies ðTC,tÞ, and an error term ðuC,it, assumed to be uncorrelated with the
right-hand-side variablesÞ. Adding these extra terms and substituting equation ð2Þ into ð1Þ gives us our main empirical equation for clean in-novation:
PATC;it 5 expðbC;Pln FPit211 bC;1ln SPILLC;it21
1 bC;2ln SPILLD;it211 bC;3ln KC;it21
1 bC;4ln KD;it211 bC;wwit1 TC;tÞhC;i1 uC;it:
ð3Þ Symmetrically, we can derive an equation for dirty innovation:
PATD;it 5 expðbD;Pln FPit211 bD;1ln SPILLC;it21
1 bD;2ln SPILLD;it211 bD;3ln KC;it21
1 bD;4ln KD;it211 bD;wwit1 TD;tÞhD;i1 uD;it:
ð4Þ 11 We construct stocks using the perpetual inventory method but show robustness to
us-ing patent flows and to considerus-ing alternative assumptions over knowledge depreciation rates. Some firms have zero lagged knowledge stock in some periods, so we also add in three dummy indicator variables for when lagged clean stock is zero, lagged dirty stock is zero, or both are zero.
Section II yielded predictions on the signs of the coefficients in these two equations. If higher fuel prices induce more clean than dirty inno-vation, then the marginal effect of the fuel price must be larger on clean
innovation than on dirty innovation:bC,P> bD,P, and we would further
ex-pect thatbC,P> 0 and bD,P< 0.12Next, for there to be path dependence in
the direction of innovation, it should be the case that ðceteris paribusÞ firms that are exposed to more dirty spillovers become more prone to
conduct dirty innovation in the future: that is,bD,2> 0 and bD,2> bC,2. In
the clean innovation equation we havebC,1> 0 and bC,1> bD,1.
Further-more, path dependence should involve similar effects working through
a firm’s own accumulated knowledge: bD,4 > 0 and bD,4 > bC,4 ðbC,3 > 0
andbC,3> bD,3Þ. Also, we would expect that the positive effect of dirty
spill-overs and dirty knowledge stocks on dirty innovation would be larger than
the effects of clean spillovers and clean knowledge stocks:bD,2> bD,1and
bD,4> bD,3. The reverse predictions should all apply for the clean equation:
bC,2< bC,1andbC,4< bC,3.
Dynamic Count Data Models with Fixed Effects To estimate equations ð3Þ and ð4Þ we use
PATz;it5 expðxitbzÞhz;i1 uz;it; ð5Þ
where z∈ fC, Dg and xit is the vector of regressors. We compare a
num-ber of econometric techniques to account for firm-level fixed effectshzi
in these Poisson models. Our baseline is an econometric model we label CFX, the control function fixed-effect estimator. It builds on the presam-ple mean scaling estimator proposed in Blundell, Griffith, and Van Reenen ð1999Þ ðsee also Blundell, Griffith, and Van Reenen 1995; Blundell, Griffith, and Windmeijer 2002Þ.
Blundell et al. suggest conditioning on the presample average of the dependent variable to proxy out the fixed effect. Like the Blundell et al. ðBGVRÞ estimator, the CFX uses a control function approach to deal with the fixed effect; but rather than using information from the presample period in the control function, we simultaneously estimate the main regression equation and a second equation allowing us to iden-tify the control function from future data ðsimilar to the idea of taking orthogonal deviations in the linear panel data literature; see Arellano 2003Þ. The full details on this are provided in appendix B, but in a nut-shell, we use CFX to deal with a potential concern with the BGVR ap-proach, namely, that it requires a long presample history of realizations 12 Note that these two stronger second conditions are not necessary for induced
ðre-directedÞ technical change as the absolute sign of the price effects will depend on the elas-ticity of substitution between cars and other goods.
of the dependent variable. However, in our data—particularly for clean— patenting is concentrated toward the end of our sample period. Be-low, we provide results using both the CFX and BGVR method as well as two other common approaches. First, we use the Hausman, Hall, and Griliches ð1984Þ method ðthe count data equivalent to the within-groups estimatorÞ even though this requires strict exogeneity, which is inconsistent with models including functions of the lagged dependent variable as we have in equations ð3Þ and ð4Þ. Second, we implement some simple linear within-groups models adding an arbitrary constant to the dependent variable before taking logarithms. We show that all these ap-proaches deliver similar qualitative results, although the CFX provides the best overall fit to the data.
IV. Data Main Data Set
In this section, we briefly present our data ðadditional details can be found in app. CÞ. Our main data are drawn from the World Patent
Sta-tistical Database ðPATSTATÞ maintained by the EPO.13Patent documents
are categorized using the International Patent Classification ðIPCÞ and national classification systems. We extract all the patents relating to clean and dirty technologies in the automotive industry. Clean is identified by patents whose technology class is specifically related to electric, hybrid, and hydrogen vehicles. Our selection of relevant IPC codes for clean technologies relies heavily on previous work by the OECD ðsee http://
www.oecd.org/environment/innovation; Haščič et al. 2009; Vollebergh
2010Þ.
Clearly, there is a debate as to how clean both electric cars and hydro-gen cars really are ðGraff Zivin, Kotchen, and Mansur 2014Þ. This will depend, by and large, on how electricity and hydrogen are being gener-ated. However, we note that in most plausible long-run scenarios, elec-tricity will be generated by renewable sources and hydrogen will be gen-erated using electrolysis. Consequently, electric and hydrogen cars would be clean. Assessing the speed of such a transition for a full optimal envi-ronmental policy is beyond the scope of this paper but is an important topic for future research.
The precise description of the IPC codes used to identify relevant clean patents can be found in panel A of table 1. Some typical IPC clas-sification codes included in the clean category are B60L11 ðelectric pro-pulsion with power supplied within the vehicleÞ and B60K6
ðarrange-13 PATSTAT can be ordered from the EPO at http://www.epo.org/searching/subscription
/raw/product-14-24.html.
TABLE 1
Definition of IPC Patent Classes for Clean and Dirty Patents
Description IPC Code
A. Clean Patents Electric vehicles:
Electric propulsion with power supplied within the vehicle B60L 11 Electric devices on electrically propelled vehicles for safety
purposes; monitoring operating variables, e.g., speed, deceleration, power consumption
B60L 3
Methods, circuits, or devices for controlling the traction– motor speed of electrically propelled vehicles
B60L 15
Arrangement or mounting of electrical propulsion units B60K 1
Conjoint control of vehicle subunits of different type or different function/including control of electric propulsion units, e.g., motors or generators/including control of energy storage means/for electrical energy, e.g., batteries or capacitors
B60W 10/08, 24, 26
Hybrid vehicles:
Arrangement or mounting of plural diverse prime movers for mutual or common propulsion, e.g., hybrid propulsion systems comprising electric motors and internal combustion engines
B60K 6
Control systems specially adapted for hybrid vehicles, i.e., vehicles having two or more prime movers of more than one type, e.g., electrical and internal combustion motors, all used for propulsion of the vehicle
B60W 20
Regenerative braking:
Dynamic electric regenerative braking B60L 7/1
Braking by supplying regenerated power to the prime mover of vehicles comprising engine-driven generators
B60L 7/20 Hydrogen vehicles/fuel cells:
Conjoint control of vehicle subunits of different type or different function; including control of fuel cells
B60W 10/28 Electric propulsion with power supplied within the vehicle—
using power supplied from primary cells, secondary cells, or fuel cells
B60L 11/18
Fuel cells; manufacture thereof H01M 8
B. Dirty Patents Internal combustion engine:
Internal combustion piston engines; combustion engines in general
F02B
Controlling combustion engines F02D
Cylinders, pistons, or casings for combustion engines; arrangement of sealings in combusion engines
F02F Supplying combusion engines with combustible mixtures or
constituents thereof
F02M
Starting of combusion engines F02N
Ignition ðother than compression ignitionÞ for internal combustion engines
F02P C. Grey Patents
Fuel efficiency of internal combustion engines:
Fuel injection apparatus F02M39-71
Idling devices for carburetors preventing flow of idling fuel F02M3/02-05
ment or mounting of hybrid propulsion systems comprising electric mo-tors and internal combustion enginesÞ. US patent 6456041 is an example
of a clean patent from our data set:14it describes a power supply system
for an electric vehicle. It was first filed by Yamaha Motor in Japan in 1998 and was then filed at the EPO and at the USPTO in 1999. The front page and technical diagram of the patent are shown in appendix figure A1.
Dirty includes patents with an IPC code that is related to the internal combustion engine. These can be found in various subcategories of the F02 group, for example, F02B ðcombustion engines in generalÞ, F02F ðcylinders, pistons, or casings for combustion enginesÞ, or F02N ðstarting of combustion enginesÞ. The full list of IPC codes used to identify dirty patents is in panel B of table 1. Each of these groups includes several dozen subclasses, and an example of the full list of subclasses for the F02F group is shown in appendix figure A2. The dirty category typically in-cludes patents covering the various parts that make up an internal combus-tion engine. For example, EPO patent 0967381 protects a cylinder head of an internal combustion engine and USPTO patent 5844336 protects a starter for an internal combustion engine.
An important feature of the dirty category is that some patents in-cluded in this group aim at improving the fuel efficiency of internal com-bustion engines, making the dirty technology less dirty. We refer to these
fuel efficiency patents as“grey” patents. In our baseline results, grey
pat-ents are included in the dirty category, but we also disaggregate the
cat-egory to estimate models separately for grey and“pure dirty” innovations
separately ðas well as splitting up the knowledge stocks along these lines on the right-hand side of the regressionsÞ. To select IPC codes for grey technologies, we use recent work at the EPO related to the new climate change mitigation patent classification ðsee Veefkind et al. 2012Þ. We complement this with information from interviews with engineers
work-ing in the automobile industry.15The list of these IPC codes is shown in
TABLE 1 (Continued )
Description IPC Code
Engine-pertinent apparatus for adding nonfuel substances or small quantities of secondary fuel to combustion-air, main fuel, or fuel-air mixture
F02M25
Electrical control of supply of combustible mixture or its constituents
F02D41 Methods of operating engines involving adding nonfuel
substances or antiknock agents to combustion air, fuel, or fuel-air mixtures of engines, the substances including nonairborne oxygen
F02B47/06
14 We use the publication numbers in this and the following patent examples. 15 We are especially indebted to Christian Hue de la Colombe for many extremely
help-ful discussions.
panel C of table 1. An example of a grey patent is EPO patent 0979940, which protects a method and device for controlling fuel injection into an internal combustion engine. Electronic fuel injection technologies con-stantly monitor and control the amount of fuel burnt in the engine, with a view to reducing the amount of fuel unnecessarily burnt, thus optimiz-ing fuel consumption. Appendix figure A3 has the front page and tech-nical diagram of this patent.
Alongside the grey fuel efficiency innovations, there are many purely dirty patents, such as EPO patent 0402091, which covers a four-cycle 12-cylinder engine ðsee app. fig. A4Þ. Fuel consumption is proportional to the number and the volume of cylinders: the average car sold in Europe has four cylinders, whereas it has six in the United States. A 12-cylinder en-gine is much more powerful than a four- or six-cylinder enen-gine, but this comes at the cost of increased fuel consumption. Twelve-cylinder en-gines are used by many carmakers for their top-range models, including Aston Martin, Audi, BMW, and Rolls Royce. These cars typically run about 15 miles per gallon, while the average new car sold in the United States in
2011 obtains 33.8 miles per gallon.16
To measure innovation, we use a count of patents by application/fil-ing date. The advantages and limitations of patentapplication/fil-ing as a measure of
in-novation have been extensively discussed.17For our purposes, there are
three advantages of using patents. First, they are available at a highly tech-nologically disaggregated level. We can distinguish innovations in the auto industry according to specific technologies, whereas R&D investment cannot be easily disaggregated. Second, R&D is not reported for small and medium-sized firms in Europe nor for privately listed firms in the United States ðthey are exempt from the accounting requirement to report R&DÞ. Third, the auto sector is an innovation-intensive sector, where patents are perceived as an effective means of protection against imitation, something
that is not true in all sectors ðCohen, Nelson, and Walsh 2000Þ.18In our
view, these considerations make patents a reasonably good indicator of in-novative activity in the auto sector.
16 See http://www.fueleconomy.gov for details on car consumption and http://www.bts
.gov/publications/national_transportation_statistics/html/table_04_23.html for US aver-ages. Note that even though much of dirty innovations are efficiency improving, this has been historically more than offset by increases in horsepower and size of cars. For example, between 1980 and 2004 the fuel efficiency of passenger cars increased by only 6.5 percent, while horsepower increased by 80 percent ðKnittel 2011Þ.
17 See Griliches ð1990Þ and OECD ð2009Þ for overviews. Dating by application is
conven-tional in the empirical innovation literature as it is much more closely timed with when the R&D was performed than the grant date.
18 Cohen et al. ð2000Þ conducted a survey questionnaire administered to 1,478 R&D labs
in the US manufacturing sector. They rank sectors according to how effective patents are considered as a means of protection against imitation and find that the top three industries according to this criterion are medical equipment and drugs, special-purpose machinery, and automobiles.
Patents do suffer from a number of limitations. They are not the only way to protect innovations, although a large fraction of the most eco-nomically significant innovations appear to have been patented ðDernis, Guellec, and van Pottelsberghe 2001Þ. Another problem is that patent values are highly heterogeneous, with most patents having a very low valuation. Finally, the number of patents that are granted for a given novation varies significantly across patent offices with concerns over in-creasing laxity in recent years particularly in the USPTO ðe.g., Jaffe and Lerner 2004Þ.
To mitigate these problems, we focus on“triadic” patents as our main
outcome measure,19which are those patents that have been taken out in
all three of the world’s major patent offices in the United States, Europe,
and Japan ðUSPTO, EPO, and JPOÞ.20Focusing on triadic patents has a
number of advantages. First, triadic patents provide us with a common measure of innovation worldwide, which is robust to administrative idi-osyncrasies of the various patent offices. For example, if the same inven-tion is covered by one patent in the United States and by two patents in Japan, all of which are part of the same triadic patent family, we will count it as one single invention. Second, triadic patents cover only the most valu-able inventions, which explains why they have been used so extensively to
capture high-quality patents.21Third, triadic patents typically protect
in-ventions that have a potential worldwide application; thus these patents are relatively independent of the countries in which they are filed.
Our data set includes 6,419 clean and 18,652 dirty triadic patents.22
Since the EPO was created in 1978, our triadic patent data start only in that year. The last year of fully comprehensive triadic data is 2005, so this
is our end year.23Our basic data set consists of all those applicants ðboth
firms and individualsÞ that applied for at least one of these clean or dirty auto patents. We identify 3,423 distinct patent holders, which breaks 19 To identify triadic patents we use the INPADOC data set in PATSTAT. For details on
the construction of patent families, see Martinez ð2010Þ.
20 Following standard practice we use all patents filed at the EPO, JPO, and USPTO. The
USPTO published ungranted patent applications only after 2001 ðwhen it changed policy in line with the other major patent officesÞ. For consistency we thus consider only triadic patents granted by the USPTO both before and after 2001. For the official definition of triadic patents and how triadic patent families are constructed, see Dernis and Kahn ð2004Þ and Martinez ð2010Þ.
21 It has been empirically demonstrated that the number of countries in which a patent
is filed is correlated with other indicators of patent value. See, e.g., Grupp ð1996, 1998Þ, Lanjouw, Pakes, and Putnam ð1998Þ, Dernis et al. ð2001Þ, Harhoff, Scherer, and Vopel ð2003Þ, Dernis and Khan ð2004Þ, and Guellec and van Pottelsberghe ð2004Þ.
22 In total, the PATSTAT data set includes 213,668 clean and 762,708 dirty patent
appli-cations across all 80 patent offices. Thus by using triadic patents we focus on the high end of the distribution of patent quality.
23 The number of triadic patents in all technologies ði.e., including patents that are
nei-ther clean nor dirtyÞ starts falling in 2006 because of time lags between application and grant date at the USPTO.
down into 2,427 companies and 996 individuals. For every patent holder we subsequently identify all the patents they filed. We also extract other pieces of information based on this sample, which we use to construct weights for prices and spillovers. For example, we identify all the other patents filed by holders of at least one clean or dirty triadic patent, which represents a total of 1,505,719 patent applications.
Tax-Inclusive Fuel Prices
To estimate the impact of a carbon tax on innovation in clean and dirty
technologies, we use information on country-level fuel prices ðFPctÞ and fuel
taxes. Data on tax-inclusive fuel prices are available from the International
Energy Agency ðIEAÞ for 25 major countries from 1978 onward.24We
con-struct a time-varying country-level fuel price defined as the average of diesel
and gasoline prices.25The average fuel price across countries for our
regres-sion sample period 1986–2005 is shown in panel a of figure 1. Although this
source of variation will be absorbed by the time dummies in our econo-metric specifications, it gives a sense of the overall evolution of prices. Fuel prices fell from the mid to late 1980s and then rose, peaking just before
the dot-com bust of 2000–2001. Prices then fell before recovering after
2003. Average fuel taxes have followed a broadly similar pattern, falling in the late 1980s, rising throughout the 1990s, and falling back in the 2000s ðpanel b of fig. 1Þ. What is more striking, however, is the high var-iability across countries of changes in the fuel price over time, much of it being driven by cross-country differences in tax policies ðsee fig. 2Þ. Fig-ure 3 illustrates this by showing the evolution of fuel price by country rel-ative to the United States normalized in 1995.
Fuel prices are available only at the country-year level, whereas our de-pendent variable has firm-level variation that we would like to exploit. A related issue is that the auto market is global, and government policies
abroad might be at least as important for a firm’s innovation decisions
24 The IEA reports some incomplete data for an additional 13 countries. We explore the
robustness of our main results to the precise range of countries considered. We find that our results emerge even if we restrict ourselves to only the 10 largest economies.
25 Diesel and gasoline are differentially taxed in many countries, which could provide an
interesting additional source of variation. However, this would also require distinguishing innovations between these categories. This is not easily possible as internal combustion en-gine patent classes do not explicitly separate between diesel and other types of enen-gines. Our interviews with engineers working in the automobile industry revealed that patent class F02B1 ðengines characterized by fuel-air mixture compressionÞ corresponds in practice mostly to gasoline engines, while patent class F02B3 ðengines characterized by air compres-sion and subsequent fuel additionÞ mostly corresponds to diesel engines. However, these are only two subclasses out of over 200 used in the paper to classify dirty patents. Conse-quently, we would not be able to classify the majority of patents into diesel or gasoline en-gines, in particular because many engine parts, such as pistons and cylinders ðsee, e.g., F02B55, internal combustion aspects of rotary pistonsÞ, are used indifferently in both types of engines.
FIG. 1.—Average fuel price ðpanel aÞ and fuel tax ðpanel bÞ, 1986–2005, for all countries
available in the IEA database. The fuel price ðrespectively, taxÞ is the average between the diesel and gasoline price ðrespectively, taxÞ. There are 25 countries underlying the graph in panel a and 24 in panel b ðtaxes are missing for South KoreaÞ. Both prices and taxes are in 2005 US dollar purchasing power parity ðPPPÞ. Source: IEA.
FIG. 2.—Country-specific changes over time. These graphs show the average annual
price of fuel and the tax on fuel for each country available in the IEA database. The fuel price ðrespectively, taxÞ is the average between the diesel and gasoline price ðrespectively, taxÞ. Prices and taxes are in 2005 US dollar PPP. Source: IEA.
as policies in the country where the company’s headquarters are located. We allow fuel prices to have a different effect across firms by noting that some geographical markets matter more than others for reasons that are idiosyncratic to an auto firm. First, auto manufacturers have different styles of vehicles reflecting their heterogeneous capabilities and brand-ing that are differentially popular dependbrand-ing on local tastes ðe.g., Berry, Levinsohn, and Pakes 1995; Goldberg 1995; Verboven 1999Þ. Second,
there is typically some home bias toward“national champion” auto
man-ufacturers in government policies and national tastes. For example, the 2008 auto bailouts in Detroit where paid for by US taxpayers, whereas the bailout of Peugeot has been shouldered by the French. The upshot of this is that auto firms display heterogeneous current and expected market shares across nations, and their R&D decisions will be more in-fluenced by prices and policies in some countries than in others.
To operationalize this idea, we construct a fuel price variable for each firm as a weighted average of fuel prices across countries based on a proxy of where the firm expects its future market to be. Our price index for firm i at time t is defined as
ln FPit5
o
c
wFP
ic0ln FPct; ð6Þ
FIG. 3.—Residuals from a regression of country-level lnðfuel pricesÞ on country and year
dummies. This illustrates the variation that is driving the identification of price effects in our main regressions. The standard deviation of the residuals is 0.107.
where FPctis the tax-inclusive fuel price in country c discussed above and
wFP
ic0 is a firm-specific weight ðthis is time invariant and uses information
only prior to the regression sample periodÞ. The weight is determined by the importance of county c as a market outlet for firm i, so we define wFP
ic0as the fraction of firm i’s patents taken out in country c. The rationale
for doing this is that a firm will seek intellectual property protection in jurisdictions where it believes it will need to sell in the future ðeven if it licenses the technology, the value of a license will depend on whether it has obtained intellectual property protection in relevant marketsÞ. For every patent applied for, we know that the patenting firm has paid the cost of legal protection in a discrete number of countries. For exam-ple, a firm may choose to enforce its rights in all EU countries or in only a subset of EU countries, say Germany and the United Kingdom. Similarly, the firm may decide to apply for patent protection in the United States but not in smaller markets. Assuming that the country distribution of a
firm’s patent portfolio is a good indicator of the firm’s expectation of
where its markets will be in the future, we can use this distribution to
con-struct a firm-specific fuel price, FPit, whose value is computed as the
weighted mean of the lnðfuel pricesÞ in the relevant markets, with weights wFP
ic0 equal to the shares of the corresponding countries in the firm’s
pat-ent portfolio. For example, if a firm had filed 30 patpat-ents, 20 in the United States and 10 in Germany, the price changes in the United States would get a weight of two-thirds and the German price changes a weight of one-third. In addition, to account for the greater importance of larger
coun-tries, we further weight by each country’s average GDP.
We calculate the weights using the patent portfolio of each company
averaged over the 1965–85 “presample” period, whereas we run
regres-sions over the period 1986–2005. This is to make sure that the weights
are weakly exogenous as patent location could be influenced by shocks to innovation. We choose 1985 as the cutoff to ensure that there is enough presample time to construct the weights. We perform robustness tests using different presample periods to check that nothing is driven by the
precise year of cutoff ðe.g., use 1965–90 as the presample period and
esti-mate the regressions from 1991 onwardÞ.
Why do we not use an alternative weighting scheme that simply re-flects where firms currently sell their products ðe.g., as in Bloom, Schan-kerman, and Van Reenen 2013Þ? First, we believe that the information on where firms choose to take patent protection is a potentially better measure because it reflects their expectations of where their future mar-kets will be. Second, there is a data constraint: although sales distribu-tions by geographic area are available for larger firms, they are not
avail-able for smaller firms—and there are many patents from these smaller
firms. We show our weights compared to sales weights for some of the largest car firms in appendix table A1: Toyota, Volkswagen, Ford, Honda,
and Peugeot. The correlation is generally high, suggesting that the
weights we choose do a reasonable job at reflecting market shares.26
The Firm’s Own Lagged Patent Stocks and Spillovers
Firm patent stocks are calculated in a straightforward manner using the
patent flows ðPATz,itÞ described above. Following Cockburn and Griliches
ð1988Þ and Peri ð2005Þ, the patent stock is calculated using the perpetual inventory method:
Kz;it5 PATz;it1 ð1 2 dÞKz;it21; ð7Þ
where z∈ fC, Dg. We take d, the depreciation of R&D capital, to be 20
per-cent, as is often assumed in the literature, but we check the robustness of our results to other plausible values.
To construct aggregate spillovers for a firm, we use information on the geographical location of the various inventors in that firm. Patent statis-tics allow us to locate an inventor geographically regardless of nationality
of the firm’s headquarters or the location of the office where the patent
was filed ðe.g., the patents of Toyota’s scientists working in US research
labs are part of this US spillover poolÞ. Implicit in our approach is the view that the geographical location of an inventor is likely to be a key de-terminant of knowledge spillovers rather than the jurisdiction over which the patent is taken out ðwhich matters more as a signal of where the mar-ket for sales is likely to beÞ. Many papers have documented the impor-tance of the geographical component of knowledge spillovers in patents and other indicators ðe.g., Henderson, Jaffe, and Trajtenberg 1993, 2005; Griffith, Lee, and Van Reenen 2011Þ.
To construct a firm-specific spillover pool, we use an empirical strategy
analogous to that for the fuel price. The spillover weight wS
ic0is the share
of all firm i’s inventors ði.e., where the inventor workedÞ in country c
be-tween 1965 and 1985. This weight is distinct from wP
ic0in equation ð6Þ as it
is based on the location of inventors who are more likely to benefit from research conducted locally. Importantly, the distribution of the patent portfolio across countries and the distribution of inventors vary consid-erably across firms. This is illustrated for the United States in appendix figure A5.
The spillover for firm i is
SPILLz;it5
o
c
wS
ic0SPILLz;ct; ð8Þ
26 One exception is that VW appears to have a much higher patent share in Germany ðits
home countryÞ than its sales would suggest.
where SPILLz,ctis the spillover pool in country c at time t. This is defined as SPILLz;ct5
o
j≠i wS jc0Kz;jt: ð9ÞThe spillover pool of a country is the sum of all other firms’ patent stocks
with a weight that depends on how many inventors the other firm has in
that country.27
As noted above, a common problem with patent data is that the value of patents is highly heterogeneous. We mitigate this problem by condi-tioning on triadic patents, which screen out the very low-value patents. But we also perform two other checks. First, we weight patents by the
number of future citations. Second, we use“biadic” patents filed at the
EPO and at the USPTO, following Cockburn and Henderson ð1994Þ, who argued that patents were important if they had been applied in at least two of the three major economic regions. Our results are robust to these two variants.
Descriptive Statistics
Figure 4 shows that aggregate triadic clean and dirty patents have been rising over time. Dirty patents increased steadily between 1978 and 1988, fell temporarily, and then rose again between 1992 and 2000, but they have been decreasing during the last 5 years of our data set. The number of clean patents was low for a decade until 1992, then began rising par-ticularly after 1995 ðat an average annual growth rate of 23 percentÞ, peaking at 724 in 2002 alone, before falling back slightly. Consequently, while the number of clean patents represented only 10 percent of the number of dirty patents filed annually during the 1980s, they repre-sented 60 percent by 2005. Descriptive statistics for our data set used in the regressions are shown in table 2. In any given year, the average num-ber of dirty patents per firm is 0.23 and the average numnum-ber of clean pat-ents is 0.08. Appendix C discusses more descriptive statistics showing more of the cross-country distribution of patent filing and citation pat-terns that are consistent with spillovers being much stronger within the two categories ðclean or dirtyÞ than between them.
27 An alternative approach would be to define the country-level spillover as
SPILLz;ct5
o
jKz;jct;
where Kz;jct5 PATz;jct1 ð1 2 dÞKz;jct21and PATz,jctis the number of patents filed by inventors
of company j located in country c at year t. Empirically, these two methods give very similar results.
We look at the top 10 patentors in clean technologies ðtable A4Þ and dirty technologies ðtable A5Þ between 1978 and 2005. Japanese and
Ger-man companies predominate, although most top companies’ portfolios
include both clean and dirty ðthe only exception is Samsung SDI, a bat-tery specialistÞ. Recall that this is based on triadic patents, and US
com-TABLE 2 Descriptive Statistics
Mean
Standard
Deviation Minimum Maximum
Clean patents ðPATC,itÞ .081 1.231 0 125
Dirty patents ðPATD,itÞ .227 3.424 0 355
Fuel price ðln FPÞ 2.276 .251 21.053 .438
Government R&D subsidies ðln R&DÞ 3.885 1.447 0 5.725
Emission regulations index 1.573 1.334 0 5
Clean spillover ðln SPILLCÞ 3.774 1.258 29.864 7.071
Dirty spillover ðln SPILLDÞ 5.401 .991 25.509 7.677
Own stock clean innovation ðln KCÞ 2.174 .790 26.718 5.740
Own stock dirty innovation ðln KDÞ 2.910 1.618 27.593 6.958
Note.—These are the values from our regression sample of 68,240 observations across 3,412 firms between 1986 and 2005. Emission regulations for maximum level of tailpipe emissions for pollutants for new automobiles are coded between 0 and 5 following Dechez-leprêtre et al. ð2012Þ. Government R&D subsidies on clean transportation is from the IEA. See app. B for exact definitions.
FIG. 4.—Number of annual clean and dirty triadic patents, 1978–2005, filed worldwide.
Source: Authors’ calculations based on the PATSTAT database.
panies tend to file disproportionately more patents in the United States
than in Europe and Japan. Tables A6–A9 report top clean and dirty
pat-entors at the EPO and at the USPTO separately. General Motors is the third-largest patentor of clean technologies at the USPTO, whereas it is
not even in the top 10 at the EPO.28
V. Results Main Results
Our main results are shown in table 3. Columns 1–3 use the number of
clean patents ða flowÞ in a firm as the dependent variable and columns 4–
TABLE 3
Regressions of Clean and Dirty Patents
Dependent Variable: Clean Patents Dependent Variable: Dirty Patents ð1Þ ð2Þ ð3Þ ð4Þ ð5Þ ð6Þ Fuel price ðln FP Þ .970*** .962** .843** 2.565*** 2.553*** 2.551*** ð.374Þ ð.379Þ ð.366Þ ð.146Þ ð.205Þ ð.194Þ
R&D subsidies ðln R&DÞ 2.005 2.006 2.006 2.005
ð.025Þ ð.024Þ ð.021Þ ð.020Þ Emission regulation 2.008 .04 ð.149Þ ð.120Þ Clean spillover ðln SPILLCÞ .268*** .301*** .266*** 2.093* 2.078 2.089 ð.076Þ ð.087Þ ð.088Þ ð.048Þ ð.067Þ ð.063Þ Dirty spillover ðln SPILLDÞ 2.168** 2.207** 2.165* .151** .132 .138* ð.085Þ ð.098Þ ð.098Þ ð.064Þ ð.082Þ ð.077Þ
Own stock clean ðln KCÞ .306*** .320*** .293*** 2.002 2.004 .021
ð.026Þ ð.027Þ ð.025Þ ð.022Þ ð.022Þ ð.020Þ
Own stock dirty ðln KDÞ .139*** .135*** .138*** .557*** .549*** .539***
ð.017Þ ð.017Þ ð.017Þ ð.031Þ ð.022Þ ð.017Þ
Observations 68,240 68,240 68,240 68,240 68,240 68,240
Firms 3,412 3,412 3,412 3,412 3,412 3,412
Note.—Standard errors are clustered at the firm level. Estimation is by the CFX method. All regressions include controls for GDP per capita, year dummies, fixed effects, and three dummies for no clean knowledge, no dirty knowledge, and no dirty or clean knowledge ðin the previous yearÞ. Fuel price is the tax-fuel price faced. R&D subsidies are public R&D ex-penditures in energy-efficient transportation. Emissions regulations are maximum levels of tailpipe emissions for pollutants from new automobiles.
* Significant at 10 percent. ** Significant at 5 percent. *** Significant at 1 percent.
28 While it is clear that there are a number of big companies active in both clean and
dirty automotive patenting, the Herfindahl index for patenting over 1978–2005 for clean innovations is 0.023, and it is 0.038 for dirty innovations, implying low concentration. The top 10 patent holders in clean account for 35.6 percent of patents over 1978–2005, whereas the corresponding figure is 46.6 percent for dirty.
6 use the flow of dirty patents. All estimates include firm fixed effects using the CFX approach ðdescribed in Sec. III and in more detail in app. BÞ, year dummies, and GDP per capita. Column 1 shows that the co-efficient on the ðtax-inclusiveÞ fuel price is positive and significant. The elasticity of 0.97 implies that a 10 percent higher fuel price is associated with about 10 percent more clean patents. The coefficients on spillovers and lagged patent stocks take signs consistent with the path dependency hypothesis. Firms that are more exposed to larger stocks of clean
innova-tion by other firms ðclean spillovers, SPILLC,it21Þ are significantly more
likely to produce clean patents, whereas those benefiting more from dirty
spillovers ðSPILLD,it21Þ are significantly less likely to innovate in clean
tech-nologies. An increase in the lagged clean spillover stock by 10 percent is
associated with an increase in a firm’s clean innovation by 2.7 percent. By
contrast, an increase in the exposure to dirty spillovers by 10 percent re-duces clean innovation by 1.7 percent.
In addition to path dependency at the economy level through spill-overs, there is also path dependency at the firm level. Column 1 of table 3 suggests that firms that have innovated in clean technologies in the past ðKC,it21Þ are much more likely to continue to innovate in clean
technol-ogies in the future, with a significant elasticity of 0.306. Interestingly, a
firm’s own history of dirty innovation ðKD,it21Þ is also associated with more
clean innovation with an elasticity of 0.139. This coefficient is, however, much smaller than the corresponding coefficient on past dirty innova-tion stocks in the dirty innovainnova-tion equainnova-tion ðcol. 4Þ, which is four times as large ð0.557Þ. In other words, firms with a history of dirty innovation are more likely to innovate in the future in either clean or dirty ðcom-pared to those with little innovationÞ, but this effect is much stronger for dirty innovations than for clean innovations, leading to path
depen-dence. Moreover, note that in column 1 the coefficient on a firm’s past
dirty innovation stock on future clean innovation ð0.139Þ is much smaller than the effect of past clean innovations on future clean innovation
ð0.306Þ.29
Columns 2 and 3 of table 3 include a measure of R&D subsidies for clean technologies and a control for emission regulations. R&D
subsi-dies are from the IEA’s Energy Technology Research Database, and the
emissions regulations index is from Dechezleprêtre, Perkins, and
Neu-mayer ð2012Þ with details in appendix C. In contrast to the proxy for car-bon taxes ðfuel pricesÞ, neither of these additional policy variables is sta-tistically significant, and the coefficients on the other variables do not change much. The absence of an R&D subsidy effect is surprising, and we explain below why when discussing table 4.
29 This effect is not predicted by the theory but could result, for instance, from
cross-technology knowledge spillovers.
Columns 4–6 of table 3 repeat the specification in the first three col-umns but use dirty patents as the dependent variable instead of clean patents. The coefficient on fuel prices is negative and significant in all columns. In column 4 a 10 percent increase in fuel prices is associated with about a 6 percent decrease in dirty innovation. The estimates on spillovers and knowledge stocks are symmetric to those in the clean equa-tion. Exposure to dirty spillovers fosters future dirty innovation, whereas clean spillovers reduce dirty patenting. The coefficients suggest that a
firm’s own history of dirty patenting has a positive association with future
dirty patenting but that there is no association between the firm’s past
clean patenting and its future dirty patents.
In summary, table 3 offers considerable support for our model. First, higher fuel prices significantly encourage clean innovation and signifi-cantly discourage dirty innovation. Second, there is path dependency in the direction of technical change: countries and firms that have a his-tory of relatively more clean ðrespectively, dirtyÞ innovation are more likely to innovate in clean ðrespectively, dirtyÞ technologies in the future. Grey Innovations
As noted above, our dirty category includes innovations relating to im-provements in the energy efficiency of internal combustion engines.
We labeled these“grey” innovations and consider disaggregating the dirty
category into these grey and purely dirty innovations. As noted in Sec-tion II, the effect of fuel prices is more ambiguous in this middle grey cat-egory. On the one hand, there are incentives to substitute research away from purely dirty into grey innovation when the fuel price rises. On the other hand, there is also an incentive to switch away from the internal combustion engines completely ðincluding greyÞ toward alternative clean vehicles.
Table 4 presents the results and shows that, as expected, the coeffi-cient on the fuel price for grey innovation in column 2 ð0.282Þ lies be-tween the coefficients on clean ðpositive at 0.848 in col. 1Þ and purely dirty ðvery negative at 20.832 in col. 3Þ. This is consistent with fuel prices hav-ing a positive effect on energy-efficient innovation, although smaller and insignificant when compared to the effect of fuel prices on purely clean innovations. Another interesting feature of the results is that the coeffi-cient on R&D subsidies is positive and significant in the grey innovation equation whereas it continues to be insignificant in the clean and purely dirty equations. This is consistent with the fact that the majority of these government subsidies are for energy efficiency ðsee app. CÞ rather than for more radical clean technologies.
Since we have also disaggregated the spillover stocks and the firm’s
own past innovation stocks into the three categories, now we have six
iables reflecting path dependency on the right-hand side of the regres-sion. The coefficients on these variables take a broadly sensible pattern, but precision has fallen as there is likely to be some collinearity issues with a large number of highly correlated variables.
Given how demanding this specification is, we find the overall results from table 4 encouraging and consistent with the theory.
Magnitude of the Fuel Price Effect on Innovation
In quantitative terms, how do our estimates compare to others in the lit-erature? Popp ð2002Þ reports short-run energy price elasticities for the impact of prices on the aggregate number of clean patents as a share
TABLE 4
Disaggregating Dirty Patents into Fuel Efficiency ðGreyÞ and Purely Dirty Dependent Variable Clean Patents ð1Þ Grey Patentsð2Þ Purely Dirty Patents ð3Þ Fuel price .848* .282 2.832*** ð.461Þ ð.398Þ ð.214Þ R&D subsidies .031 .081** 2.02 ð.047Þ ð.034Þ ð.030Þ Clean spillover .333** 2.171* 2.014 ð.165Þ ð.098Þ ð.094Þ Grey spillover .215 .173 .235** ð.228Þ ð.112Þ ð.102Þ
Purely dirty spillover 2.509 .045 2.208
ð.377Þ ð.136Þ ð.161Þ
Own stock clean .379*** 2.005 .047
ð.090Þ ð.035Þ ð.035Þ
Own stock grey .185* .418*** 2.141***
ð.106Þ ð.035Þ ð.025Þ
Own stock purely dirty 2.011 .192*** .544***
ð.066Þ ð.038Þ ð.026Þ
Observations 68,240 68,240 68,240
Firms 3,412 3,412 3,412
Note.—Standard errors are clustered at the firm level. Estimation is by the CFX method. This table disaggregates the dirty patents into those that are “grey” ðrelated to fuel efficiencyÞ and those that are not ð“purely dirty”Þ. We construct all spillovers and own past stocks on the basis of this disaggregation and include on the right-hand side ðhence two ex-tra terms compared to table 3Þ. We estimate two dirty equations: one in which grey inno-vations are the dependent variable ðin col. 2Þ and one for the purely dirty ðin col. 3Þ. All regressions include controls for GDP per capita, year dummies, fixed effects, and four dum-mies for no own innovations in ðiÞ clean, ðiiÞ grey, ðiiiÞ dirty, and ðivÞ no clean, grey, or purely dirty in the previous year. Fuel price is the tax-inclusive fuel price faced. R&D subsidies are public R&D expenditures in energy-efficient transportation.
* Significant at 10 percent. ** Significant at 5 percent. *** Significant at 1 percent.
